From Predictive Coding to EBPM: A Novel DIME Integrative Model for Recognition and Cognition

Predictive Coding (PC) frameworks claim to model recognition via prediction–error loops, but they often lack explicit biological implementation of fast familiar recognition and impose latency that limits real-time robotic control. We begin with Experience-Based Pattern Matching (EBPM), a biologically grounded mechanism inspired by neural engram reactivation, enabling near-instantaneous recognition of familiar stimuli without iterative inference. Building upon this, we propose Dynamic Integrative Matching and Encoding (DIME), a hybrid system that relies on EBPM under familiar and low-uncertainty conditions and dynamically engages PC when confronted with novelty or high uncertainty. We evaluate EBPM, PC, and DIME across multiple image datasets (MNIST, Fashion-MNIST, CIFAR-10) and on a robotic obstacle-course simulation. Results from multi-seed experiments with ablation and complexity analyses show that EBPM achieves minimal latency (e.g., ~0.03 ms/ex in MNIST, ~0.026 ms/step in robotics) but poor performance in novel or noisy cases; PC exhibits robustness at a high cost; DIME delivers strong trade-offs—boosted accuracy in familiar clean situations (+4–5% over EBPM on CIFAR-10), while cutting PC invocations by ~50% relative to pure PC. Our contributions: (i) formalizing EBPM as a neurocomputational algorithm built from biologically plausible principles, (ii) developing DIME as a dynamic EBPM–PC integrator, (iii) providing ablation and complexity analyses illuminating component roles, and (iv) offering empirical validation in both perceptual and embodied robotic scenarios—paving the way for low-latency recognition systems.